Human Skills at the Center of Technology

1. Forget about AI, Let’s Talk about IA

Artificial intelligence will become increasingly adept at routine, or scripted tasks, but according to McKinsey Global Institute — which comprehensively analyzed 800 occupations — only five percent of jobs are comprised of tasks for which full technological automation is projected to be possible.

Moreover, their 2017 study concluded that, for 60 percent of occupations analyzed, only 30 percent of the tasks within a job had potential for automation. What this means is that — a far cry from the coming robot apocalypse — we might consider how the letters “AI” could be better arranged (something that pioneers in the field such as MIT’s J.C.R. Licklider suggested more than 50 years ago). “Intelligence Augmentation” or “Amplification” suggests that our “IA” supplements, rather than replaces, humans.

2. The Soft Skills Matter More than Ever

In a world where machine learning and AI take over the routine tasks, what remains are tasks that are non-routine, dynamic, and require flexibility or improvisation. David Deming, an economist at the Harvard Graduate School of Education, talks about this world of higher complexity, where the greatest job growth is not in pure technical skills. His research focuses on the overlooked reality that the fastest job growth is in what he calls “high math, high social” jobs. People require basic technical versatility, but they also require an ability to communicate and optimize productivity through collaboration.

3. It’s Time for 20 Percent Talent

Google is famous for its “20 percent time,” the rough percentage of time that employees can dedicate to ancillary endeavors, research interests, or new ideas. Kim Scott, once the head of Google’s AdSense and today author of the book Radical Candor, talks about the need to hire both “Rock Stars” and “Superstars.” Each team needs some percentage of people who are highly competent at the core job, or who are the bedrock of the team. These are her so-called “rock stars.” Equally, each team needs some “superstars,” or those who might be a bit less interested in core job mastery, but who are extremely high energy and inject new entrepreneurial ideas.

In parallel, while technical literacy is necessary, we might consider these lessons by constructing teams with more than merely technical people with sought-after STEM degrees. Just as Google has 20 percent time, perhaps we ought to consider composing teams with “20 percent talent.” Twenty percent of every team should be comprised of individuals with an orthogonal skill set. If you’re hiring for a 20-person data science team, there’s no reason four people on the team shouldn’t be philosophers, psychologists, and anthropologists — people who see the data from another angle.

Too many people with shared backgrounds or viewpoints can undermine a team’s ability to ask hard questions and tame potential bias. Nobel Prize winning behavioral economist Daniel Kahneman talks about Inside versus Outside bias. Provided that there is psychological safety, and the ability for all to contribute, diversity matters because it mitigates the bias that can come as a result of too many insiders thinking alike.

Just as you need superstars to mix things up, you need a few outsiders to keep it real.